Leveraging institutional data to understand student perceptions of teaching in large engineering classes
2017 IEEE Frontiers in Education Conference (FIE) (2017)
Indianapolis, IN, USA
Oct. 18, 2017 to Oct. 21, 2017
Michelle Soledad , Department of Engineering Education / Electrical Engineering Department, Virginia Tech / Ateneo de Davao University
Jacob Grohs , Department of Engineering Education
Sreyoshi Bhaduri , Department of Engineering Education
Jennifer Doggett , Department of Mining & Minerals Engineering
Jaime Williams , Office of Assessment and Evaluation Virginia Tech
Steven Culver , Office of Assessment and Evaluation Virginia Tech
A global push to pursue careers in engineering has led to an increase in enrollment in engineering programs. However, rising student populations have led institutions to make compromises in order to effectively manage existing resources and rising costs, such as resorting to large classes despite evidence that they may be detrimental to student learning. Recognizing that large classes are both a necessity for institutions and a challenge for the instructors who teach them, we seek ways to help faculty create effective learning environments despite the difficulties posed by this setting. Developing an effective learning environment requires instructors to reflect and consider input from various sources, including students. A source of data for student input are student perceptions of teaching surveys. This paper used the MUSIC Model of Academic Motivation as basis to characterize qualitative data from student surveys with respect to two of the five MUSIC dimensions: Success and Caring. We allowed categorical variables to emerge from qualitative data and investigated how quantitative results from the student evaluation (e.g., Did the instructor present the material clearly?) varied across categories. The manually-analyzed text data were also used to explore text analytics as a qualitative analysis technique for course evaluation surveys.
Multiple signal classification, Education, Natural language processing, Data models, Instruments, Analytical models, Manuals
M. Soledad, J. Grohs, S. Bhaduri, J. Doggett, J. Williams and S. Culver, "Leveraging institutional data to understand student perceptions of teaching in large engineering classes," 2017 IEEE Frontiers in Education Conference (FIE), Indianapolis, IN, USA, 2017, pp. 1-8.